R. K. Brojen Singh

Effect of monomeric and oligomeric sugar osmolytes on ΔGD, the Gibbs energy of stabilization of the protein at different pH values: Is the sum effect of monosaccharide individually additive in a mixture?

Thermal denaturation curves of ribonuclease-A were measured by monitoring changes in the far-UV circular dichroism (CD) spectra in the presence of different concentrations of six sugars (glucose, fructose, galactose, sucrose, raffinose and stachyose) and mixture of monosaccharide constituents of each oligosaccharide at various pH values in the range of 6.0-2.0. These measurements gave values of T(m) (midpoint of denaturation), DeltaH(m) (enthalpy change at T(m)), DeltaC(p) (constant-pressure heat capacity change) under a given solvent condition. Using these values of DeltaH(m), T(m) and DeltaC(p) in appropriate thermodynamic relations, thermodynamic parameters at 25 degrees C, namely, DeltaG(D)(o) (Gibbs energy change), DeltaH(D)(o) (enthalpy change), and DeltaS(D)(o) (entropy change) were determined at a given pH and concentration of each sugar (including its mixture of monosaccharide constituents). Our main conclusions are: (i) each sugar stabilizes the protein in terms of T(m) and DeltaG(D)(o), and this stabilization is under enthalpic control, (ii) the protein stabilization by the oligosaccharide is significantly less than that by the equimolar concentration of the constituent monosaccharides, and (iii) the stabilization by monosaccharides in a mixture is fully additive. Furthermore, measurements of the far- and near-UV CD spectra suggested that secondary and tertiary structures of protein in their native and denatured states are not perturbed on the addition of sugars.

The enhancement of stability of p53 in MTBP induced p53-MDM2 regulatory network.

We have modeled an MTBP-MDM2-p53 regulatory network by integrating p53-MDM2 autoregulatory model (Proctor and Gray, 2008) with the effect of a cellular protein MTBP (MDM2 binding protein) which is allowed to bind with MDM2 (Brady et al., 2005). We study this model to investigate the activation of p53 and MDM2 steady state levels induced by MTBP protein under different stress conditions. Our simulation results in three approaches namely deterministic, Chemical Langevin equation and stochastic simulation of Master equation show a clear transition from damped limit cycle oscillation to fixed point oscillation during a certain time period with constant stress condition in the cell. This transition is the signature of transition of p53 and MDM2 levels from activated state to stabilized steady state levels. We present various phase diagrams to show the transition between unstable and stable states of p53 and MDM2 concentration levels and also their possible relations among critical value of the parameters at which the respective protein level reach stable steady states. In the stochastic approach, the dynamics of the proteins become noise induced process depending on the system size. We found that this noise enhances the stability of the p53 steady state level.

Brief Communication: Microtubule dynamics regulated by stathmin

Microtubules perform a variety of functions which lead to the complex regulation of intracellular transport and cell division. However, the regulation of microtubule growth is not clearly known. Based on a recent experimental finding, we explore the possibility of spatial regulation of microtubule growth by stathmin-tubulin interaction gradients. Computer simulation of the model with stathmin-tubulin interaction gradients gave regulated growth as seen in experiments. In future, the stathmin-tubulin interaction gradients can be made dynamic and its impact on the microtubule growth can be explored.

Apoptosis regulatory protein–protein interaction demonstrates hierarchical scale-free fractal network

Dysregulation or inhibition of apoptosis favors cancer and many other diseases. Understanding of the network interaction of the genes involved in apoptotic pathway, therefore, is essential, to look for targets of therapeutic intervention. Here we used the network theory methods, using experimentally validated 25 apoptosis regulatory proteins and identified important genes for apoptosis regulation, which demonstrated a hierarchical scale-free fractal protein-protein interaction network. TP53, BRCA1, UBIQ and CASP3 were recognized as a four key regulators. BRCA1 and UBIQ were also individually found to control highly clustered modules and play an important role in the stability of the overall network. The connection among the BRCA1, UBIQ and TP53 proteins was found to be important for regulation, which controlled their own respective communities and the overall network topology. The feedback loop regulation motif was identified among NPM1, BRCA1 and TP53, and these crucial motif topologies were also reflected in high frequency. The propagation of the perturbed signal from hubs was found to be active upto some distance, after which propagation started decreasing and TP53 was the most efficient signal propagator. From the functional enrichment analysis, most of the apoptosis regulatory genes associated with cardiovascular diseases and highly expressed in brain tissues were identified. Apart from TP53, BRCA1 was observed to regulate apoptosis by influencing motif, propagation of signals and module regulation, reflecting their biological significance. In future, biochemical investigation of the observed hub-interacting partners could provide further understanding about their role in the pathophysiology of cancer.

The Dynamics of Stress p53-Mdm2 Network Regulated by p300 and HDAC1

We construct a stress p53-Mdm2-p300-HDAC1 regulatory network that is activated and stabilised by two regulatory proteins, p300 and HDAC1. Different activation levels of observed due to these regulators during stress condition have been investigated using a deterministic as well as a stochastic approach to understand how the cell responds during stress conditions. We found that these regulators help in adjusting p53 to different conditions as identified by various oscillatory states, namely fixed point oscillations, damped oscillations and sustain oscillations. On assessing the impact of p300 on p53-Mdm2 network we identified three states: first stabilised or normal condition where the impact of p300 is negligible, second an interim region where p53 is activated due to interaction between p53 and p300, and finally the third regime where excess of p300 leads to cell stress condition. Similarly evaluation of HDAC1 on our model led to identification of the above three distinct states. Also we observe that noise in stochastic cellular system helps to reach each oscillatory state quicker than those in deterministic case. The constructed model validated different experimental findings qualitatively.

Effect of oligosaccharides and their monosaccharide mixtures on the stability of proteins: a scaled particle study.

Abstract Experimental results of RNase-A stabilization by sugar osmolytes show that the change in the Gibbs free energy (Δ G D) associated with the equilibrium, N (native) state ↔ D (denatured) state of the protein in the presence of equimolar mixture of monosaccharides is larger than that of the corresponding oligosaccharides at a given temperature and pH. However, at the molar scale, ΔG D obtained in the presence of an oligosaccharide is much higher as compared with ΔG D obtained using individual monosaccharide. We used scaled particle theory (SPT) to explain these experimental observations. The effective length, called Tolmans length that describes the curvature correlations to a surface area or surface tension and in turn contributes to the change in free energy, is discussed. Tolmans length is higher for corresponding monomer mixture than the oligosaccharide molecules. Based on SPT analysis, a geometrical model is proposed for clustering of monosaccharides in the mixture due to high particle density. The cluster is presumed to have weak interaction among them due to larger hydrodynamic radius than that of the bonded molecules of oligosaccharides.

Bifurcation in Cell Cycle Dynamics Regulated by p53

We study the regulating mechanism of p53 on the properties of cell cycle dynamics in the light of the proposed model of interacting p53 and cell cycle networks via p53. Irradiation (IR) introduce to p53 compel p53 dynamics to suffer different phases, namely oscillating and oscillation death (stabilized) phases. The IR induced p53 dynamics undergo collapse of oscillation with collapse time Δt which depends on IR strength. The stress p53 via IR drive cell cycle molecular species MPF and cyclin dynamics to different states, namely, oscillation death, oscillations of periods, chaotic and sustain oscillation in their bifurcation diagram. We predict that there could be a critical Δt c induced by p53 via IR c, where, if Δt〈Δt c the cell cycle may come back to normal state, otherwise it will go to cell cycle arrest (apoptosis).

Dynamical states, possibilities and propagation of stress signal

The stress driven dynamics of Notch-Wnt-p53 cross-talk is subjected to a few possible dynamical states governed by simple fractal rules, and allowed to decide its own fate by choosing one of these states which are contributed from long range correlation with varied fluctuations due to active molecular interaction. The topological properties of the networks corresponding to these dynamical states have hierarchical features with assortive structure. The stress signal driven by nutlin and modulated by mediator GSK3 acts as anti-apoptotic signal in this system, whereas, the stress signal driven by Axin and modulated by GSK3 behaves as anti-apoptotic for a certain range of Axin and GSK3 interaction, and beyond which the signal acts as favor-apoptotic signal. However, this stress system prefers to stay in an active dynamical state whose counterpart complex network is closest to hierarchical topology with exhibited roles of few interacting hubs. During the propagation of stress signal, the system allows the propagator pathway to inherit all possible properties of the state to the receiver pathway/pathways with slight modifications, indicating efficient information processing and democratic sharing of responsibilities in the system via cross-talk. The increase in the number of cross-talk pathways in the system favors to establish self-organization.

Identification of key regulators and their controlling mechanism in a combinatorial apoptosis network: a systems biology approach

An experimentally validated set of apoptosis-regulatory proteins was subjected to network analysis, depicting a scale-free hierarchical fractal network. The power-law distribution of the various topological properties of the network revealed the fractal nature of the network, a signature of self-organization of the network where the network maintained the democratic constitution of nodes at various levels and showed the absence of the centrality-lethality control system. Even though network breakdown under the absence of the centrality-lethality rule of hub removal did not happen, the change in the topological properties of the network could be observed. Depending on the amount of change observed in topological properties, we identified a few proteins (hubs) which could be functionally important in the combinatorial apoptosis gene regulatory network. The crosstalk of these important proteins within the network along with functional modules probably tries to maintain the structural features of the network. NFKB1 was found to be the most efficient signal transducer followed by SP1. In addition, hsa-let-7a controlled the modules independently, revealing its importance in the incoherent and coherent types of feed forward loop motif analysis. The sub-modules and sub-sub-modules predicted bi-fan and multi-layer-perceptron motifs, suggesting the role of multifunctional signals in regulating apoptosis. Finally, SP1, NFKB1 and hsa-let-7a were observed to regulate apoptosis by influencing motifs, signal transduction, and module regulation, which was validated through the removal of hubs, signifying their biological importance in association with cancers.

Intercellular synchronization of diffusively coupled Ca(2+) oscillators.

We examine the synchrony in the dynamics of localized [Ca2 + ]i oscillations among a group of cells exhibiting such complex Ca2 +  oscillations, connected in the form of long chain, via diffusing coupling where cytosolic Ca2 +  and inositol 1,4,5-triphosphate are coupling molecules. Based on our numerical results, we could able to identify three regimes, namely desynchronized, transition and synchronized regimes in the (T − ke) (time period-coupling constant) and (A − ke) (amplitude-coupling constant) spaces which are supported by phase plots (Δϕ verses time) and recurrence plots, respectively. We further show the increase of synchronization among the cells as the number of coupling molecules increases in the (T − ke) and (A − ke) spaces.